Predicting Yield Losses in Rice Mixed-Weed Species Infestations in California

Abstract

Although many pests constrain rice production, weeds are considered to be the major barrier to achieving optimal yields. A predictive model based on naturally occurring mixed-species infestations in the field would enable growers to target the specific weed group that is the greatest contributor to yield loss, but as of now no such models are available. In 2013 and 2014, two empirical hyperbolic models were tested using the relative cover at canopy closure of groups of weed species as independent variables: grasses, sedges, broadleaves, grasses and sedges combined, grasses and broadleaves combined, and all weed species combined. Models were calibrated using data from experiments conducted at the California Rice Experiment Station, in Biggs, CA, and validated across four sites over 2 years, for a total of 7 site-year combinations. Of the three major weed groups, grasses, sedges, and broadleaves, the only groups positively related to yield loss in the multispecies infestation were grasses. At the model calibration site, grasses and sedges combined best predicted yield loss (corrected Akaike information criterion [AICc] = −21.5) in 2013, and grasses alone best predicted yield loss (AICc = −19.0) in 2014. Across the validation sites, the model using grasses and sedges combined was the best predictor in 5 out of 7 site-years. Accuracy of the predicted values at the model validation sites ranged from 6% mean average error to 17% mean average error. No single model and set of parameters accurately predicted losses across all years and locations, but relative cover of grasses and sedges combined at canopy closure was the best estimate over the most sites and years. Nomenclature: Rice, Oryza sativa L.

Machine learning models as an alternative to determine productivity losses caused by weeds

“…However, comparative results between ANNs and traditional sigmoids strongly suggest that this challenge can be overcome by applying machine learning models. The main advantage of ANNs over traditional models was the possibility of adding different inputs involved in the competition between weed and crop 65,66 . This analysis considering different inputs is not applied in simplistic sigmoid models used to model competition between weeds and crops.…”

“…The main advantage of ANNs over traditional models was the possibility of adding different inputs involved in the competition between weed and crop. 65,66 This analysis considering different inputs is not applied in simplistic sigmoid models used to model competition between weeds and crops. Consequently, the other parameters related to weeds are discussed only descriptively.…”

Combining stale seedbed with deep rice planting: a novel approach to herbicide resistance management?

“…barnyardgrass, early watergrass, and late watergrass were by far the most commonly observed weeds in the study field, followed by ducksalad, smallflower umbrella sedge, ricefield bulrush, bearded sprangletop, and Ammania spp. (Brim-DeForest et al 2017a, 2017b. Young seedlings of Echinochloa species are difficult to differentiate in the field; therefore, these species were grouped together as a genus for density counts and analysis.…”

Florpyrauxifen-Benzyl Selectivity to Rice

“…1 See Table S3 for temperature treatments. 2 Florpyrauxifen-benzyl rate in g ai ha −1 . 3 Abbreviations: DAA, days after application.…”

“…The main biotic factor that decreases the yield and quality of rice are weeds (unwanted plants in the field), exhibiting the greatest potential for yield losses globally (34%), which is greater compared to insects (18%) and phytopathogens (15%) [1]. Currently, rice yield loss due to weeds is estimated at around 10% [1]; however, up to 100% losses have been reported in the absence of control [2]. The most important weeds in rice areas include the weedy rice complex (Oryza sativa L.), the Echinochloa spp.…”